45 references to RoleMappedData
Microsoft.ML.Core (13)
Data\RoleMappedSchema.cs (4)
31
/// that case, please use the <see cref="
RoleMappedData
"/> class.
33
/// Note that there is no need for components consuming a <see cref="
RoleMappedData
"/> or <see cref="RoleMappedSchema"/>
39
/// <seealso cref="
RoleMappedData
"/>
112
/// for giving to constructors of <see cref="RoleMappedSchema"/> and <see cref="
RoleMappedData
"/>.
EntryPoints\PredictorModel.cs (1)
51
internal abstract void PrepareData(IHostEnvironment env, IDataView input, out
RoleMappedData
roleMappedData, out IPredictor predictor);
Prediction\ITrainer.cs (2)
93
public static IPredictor Train(this ITrainer trainer,
RoleMappedData
trainData)
104
public static TPredictor Train<TPredictor>(this ITrainer<TPredictor> trainer,
RoleMappedData
trainData) where TPredictor : IPredictor
Prediction\TrainContext.cs (6)
21
public
RoleMappedData
TrainingSet { get; }
28
public
RoleMappedData
ValidationSet { get; }
36
public
RoleMappedData
TestSet { get; }
53
public TrainContext(
RoleMappedData
trainingSet,
RoleMappedData
validationSet = null,
RoleMappedData
testSet = null, IPredictor initialPredictor = null)
Microsoft.ML.Data (9)
Commands\EvaluateCommand.cs (2)
91
/// Both take a <see cref="
RoleMappedData
"/> as input. The <see cref="
RoleMappedData
"/> is assumed to contain all the column
Evaluators\EvaluatorBase.cs (1)
15
/// <see cref="GetPerInstanceMetricsCore"/>. Note that the input <see cref="
RoleMappedData
"/> is assumed to contain all the column
Evaluators\EvaluatorUtils.cs (1)
747
/// <param name="perInstance">The array of scored data views to evaluate. These are passed as <see cref="
RoleMappedData
"/>
Evaluators\MamlEvaluator.cs (3)
17
/// The input <see cref="
RoleMappedData
"/> to the <see cref="IEvaluator.Evaluate"/> and the <see cref="IEvaluator.GetPerInstanceMetrics"/> methods
19
/// evaluation should be searched for by name in the <see cref="
RoleMappedData
.Schema"/>.
51
/// methods create a new <see cref="
RoleMappedData
"/> containing all the columns needed for evaluation, and call the corresponding
Training\TrainerUtils.cs (2)
16
/// Options for creating a <see cref="TrainingCursorBase"/> from a <see cref="
RoleMappedData
"/> with specified standard columns active.
252
/// Create a row cursor set for the <see cref="
RoleMappedData
"/> with the indicated standard columns active.
Microsoft.ML.Ensemble (2)
EntryPoints\CreateEnsemble.cs (2)
315
/// This method takes a <see cref="
RoleMappedData
"/> as input, saves it as an in-memory <see cref="ZipArchive"/>
350
/// as a <see cref="
RoleMappedData
"/>, and the second as a double byte array and a string array. The double
Microsoft.ML.FastTree (7)
FastTreeClassification.cs (1)
187
var
trainData = context.TrainingSet;
FastTreeRanking.cs (1)
141
var
trainData = context.TrainingSet;
FastTreeRegression.cs (1)
111
var
trainData = context.TrainingSet;
FastTreeTweedie.cs (1)
120
var
trainData = context.TrainingSet;
RandomForestClassification.cs (1)
214
var
trainData = context.TrainingSet;
RandomForestRegression.cs (1)
353
var
trainData = context.TrainingSet;
TreeEnsembleFeaturizer.cs (1)
645
var
data = TrainAndScoreTransformer.CreateDataFromArgs(ch, input, args);
Microsoft.ML.KMeansClustering (1)
KMeansPlusPlusTrainer.cs (1)
200
var
data = context.TrainingSet;
Microsoft.ML.Mkl.Components (1)
OlsLinearRegression.cs (1)
156
var
examples = context.TrainingSet;
Microsoft.ML.Recommender (2)
RecommenderUtils.cs (2)
14
/// Check if the considered data, <see cref="
RoleMappedData
"/>, contains column roles specified by <see cref="MatrixColumnIndexKind"/> and <see cref="MatrixRowIndexKind"/>.
40
/// Checks whether a column kind in a <see cref="
RoleMappedData
"/> is unique, and its type
Microsoft.ML.StandardTrainers (10)
Standard\LogisticRegression\LbfgsPredictorBase.cs (1)
438
var
data = context.TrainingSet;
Standard\MulticlassClassification\MetaMulticlassTrainer.cs (1)
120
var
data = context.TrainingSet;
Standard\MulticlassClassification\MulticlassNaiveBayesTrainer.cs (1)
135
var
data = context.TrainingSet;
Standard\Online\OnlineLinear.cs (3)
201
/// Called by <see cref="TrainCore(IChannel,
RoleMappedData
, TrainStateBase)"/> at the start of a pass over the dataset.
212
/// Called by <see cref="TrainCore(IChannel,
RoleMappedData
, TrainStateBase)"/> after a pass over the dataset.
288
var
data = context.TrainingSet;
Standard\SdcaBinary.cs (3)
2205
/// It's used at the end of <see cref="TrainCore(IChannel,
RoleMappedData
, LinearModelParameters, int)"/> to finalize the trained model.
2310
/// Given weights and bias trained in <see cref="SgdBinaryTrainerBase{TModelParameters}.TrainCore(IChannel,
RoleMappedData
, LinearModelParameters, int)"/>,
2437
/// a calibrator would be added after <see cref="SgdBinaryTrainerBase{TModelParameters}.TrainCore(IChannel,
RoleMappedData
, LinearModelParameters, int)"/>
Standard\Simple\SimpleTrainers.cs (1)
253
var
data = context.TrainingSet;